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capillaryPCR.Rd
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\name{capillaryPCR}
\alias{capillaryPCR}
\docType{data}
\title{
capillary convective PCR
}
\description{
The capillary Convective PCR (ccPCR) is a modified device of the ccPCR system
proposed by Chou et
al. 2013.
}
\usage{data(capillaryPCR)}
\format{
A data frame with 1844 observations on the following 10 variables.
\describe{
\item{\code{t.121205}}{Elapsed time during amplification}
\item{\code{ED.121205}}{a numeric vector}
\item{\code{t.121128}}{Elapsed time during amplification}
\item{\code{ED.121128}}{a numeric vector}
\item{\code{t.121130.1}}{Elapsed time during amplification}
\item{\code{ED.121130.1}}{a numeric vector}
\item{\code{t.121130.2}}{Elapsed time during amplification}
\item{\code{ED.121130.2}}{a numeric vector}
\item{\code{t.121130.3}}{Elapsed time during amplification}
\item{\code{ED.121130.3}}{a numeric vector}
}
}
\details{
Modified version of the capillary convective tube isothermal heater heater by
Chou et al. 2011. As heating system a conventional block heat was used. On the
top of the heating block, we placed for the uptake of the capillaries an
aluminum block (8 mm height) in which four holes (3.2 mm diameter and 3.0 mm
depth with round shaped bottom) were drilled. The capillaries are regular 100
micro L Roche LightCycler(R). These glass capillaries have a round shaped
closed bottom (2.3 mm inner diameter and 3.2 mm outer diameter). An "ESE-Log"
detector (QIAGEN Lake Constance) was used for the real time fluorescent
measurements, which was mounted in a distance of 5-10 mm next to the
capillary. The PCR was performed with SYBR(R) Green fluorescent intercalating
dye. Thereof the ESE-Log has in one channel the excitation at 470 nm and the
detection at 520 nm. The data was recorded by the FL Digital Software (QIAGEN
Lake Constance) and the exported text based raw data.
}
\source{
Ralf Himmelreich, IMM, Mainz, Germany
}
\references{
Chou, W., Chen, P., Miao Jr, M., Kuo, L., Yeh, S. and Chen, P. (2011). Rapid
DNA amplification in a capillary tube by natural convection with a single
isothermal heater. Biotech. 50, 52-57.
}
\examples{
# First example
data(capillaryPCR)
plot(NA, NA, xlim = c(0,80), ylim = c(0,1300), xlab = "time [min]",
ylab = "Voltage [micro V]", main = "ccPCR - Raw Data")
for (i in c(1,3,5,7)) {
lines(capillaryPCR[, i], capillaryPCR[, i+1], type = "b", pch = 20)
}
abline(h = 290, v = c(18, 23, 35))
legend(60,800, c("Run 1", "Run 2", "Run 3", "Control"), pch = 20, lwd = 2)
par(mfrow = c(2,1))
type <- c("mova", "spline", "savgol")
plot(NA, NA, xlim = c(0,80), ylim = c(-50,1100), xlab = "time [min]",
ylab = "Voltage [micro V]", main = "ccPCR with mova,
spline and savgol")
for (i in 1:3) {
for (j in c(1,3,5,7)) {
tmp <- data.frame(na.omit(capillaryPCR[, j]),
na.omit(capillaryPCR[, j+1]))
tmp.sm <- smoother(tmp[, 1], tmp[, 2], method = type[i])
lines(data.frame(tmp[, 1], tmp.sm), type = "b", pch = 20, cex = 0.5,
col = i)
}
}
abline(h = 50, v = c(17.5, 21.3, 32.9))
legend(0,1000, c("mova", "spline", "savgol"), pch = 20, lwd = 2, col = c(1:3))
plot(NA, NA, xlim = c(10,40), ylim = c(-50,100), xlab = "time [min]",
ylab = "Voltage [micro V]", main = "ccPCR with mova,
spline and savgol")
for (i in 1:3) {
for (j in c(1,3,5,7)) {
tmp <- data.frame(na.omit(capillaryPCR[, j]),
na.omit(capillaryPCR[, j+1]))
tmp.sm <- smoother(tmp[, 1], tmp[, 2], method = type[i])
lines(data.frame(tmp[, 1], tmp.sm), type = "b", pch = 20, cex = 0.5,
col = i)
}
}
abline(h = 50, v = c(17.5, 21.3, 32.9))
legend(35,80, c("mova", "spline", "savgol"), pch = 20, lwd = 2, col = c(1:3))
par(mfrow = c(1,1))
# Second example
method <- c("lowess","mova","savgol","smooth","spline", "supsmu")
plot(NA, NA, xlim = c(0,100), ylim = c(-50,1100), xlab = "time [min]",
ylab = "Voltage [micro V]", main = "capillary convective PCR")
for (i in 1:length(method)) {
for (j in c(1,3,5,7)) {
tmp <- data.frame(na.omit(capillaryPCR[, j]),
na.omit(capillaryPCR[, j+1]))
tmp.sm <- smoother(tmp[, 1], tmp[, 2], method = method[i])
lines(data.frame(tmp[, 1], tmp.sm), type = "l", pch = 20, cex = 0.5,
col = i)
}
}
legend(0,1000, method, pch = 20, lwd = 2, col = 1:length(method))
par(fig = c(0.5,1,0.15,0.75), new = TRUE)
plot(NA, NA, xlim = c(10,40), ylim = c(-50,200), xlab = "time [min]",
ylab = "Voltage [micro V]", main = "")
for (i in 1:length(method)) {
for (j in c(1,3,5,7)) {
tmp <- data.frame(na.omit(capillaryPCR[, j]),
na.omit(capillaryPCR[, j+1]))
tmp.sm <- smoother(tmp[, 1], tmp[, 2], method = method[i])
lines(data.frame(tmp[, 1], tmp.sm), type = "l", pch = 20, cex = 0.5,
col = i)
}
}
legend(0,1000, method, pch = 20, lwd = 2, col = 1:length(method))
# Third example
# Comparison of Lowess, Moving average and splines to smooth amplification
# curve data of
# a capillary convective PCR.
par(mfrow = c(2,1), cex = 0.9)
plot(NA, NA, xlim = c(10,40), ylim = c(-50,200), xlab = "time [min]",
ylab = "Voltage [micro V]", main = "ccPCR - Moving average")
movaww <- seq(1,17,4)
for (i in 1:length(movaww)) {
for (j in c(1,3,5,7)) {
tmp <- data.frame(na.omit(capillaryPCR[, j]),
na.omit(capillaryPCR[, j+1]))
tmp.out <- smoother(tmp[, 1], tmp[, 2], method = "mova",
movaww = movaww[i])
lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20,
cex = 0.5, col = i)
}
}
text(10,160, "A)", cex = 3)
legend(25,200, paste("movaww : ", movaww), pch = 20, lwd = 2,
col = 1:length(movaww))
plot(NA, NA, xlim = c(10,40), ylim = c(-50,200), xlab = "time [min]",
ylab = "Voltage [micro V]", main = "ccPCR - Cubic Spline")
df.fact <- seq(0.5,0.9,0.1)
for (i in 1:length(df.fact)) {
for (j in c(1,3,5,7)) {
tmp <- data.frame(na.omit(capillaryPCR[, j]),
na.omit(capillaryPCR[, j+1]))
tmp.out <- smoother(tmp[, 1], tmp[, 2], method = "smooth",
df.fact = df.fact[i])
lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20,
cex = 0.5, col = i)
}
}
text(10,160, "B)", cex = 3)
legend(25,200, paste("df.fact : ", df.fact), pch = 20, lwd = 2,
col = 1:length(df.fact))
par(mfrow = c(2,1), cex = 0.9)
plot(NA, NA, xlim = c(10,40), ylim = c(-50,200), xlab = "time [min]",
ylab = "Voltage [micro V]", main = "ccPCR - Lowess")
f <- seq(0.01,0.2,0.04)
for (i in 1:length(f)) {
for (j in c(1,3,5,7)) {
tmp <- data.frame(na.omit(capillaryPCR[, j]),
na.omit(capillaryPCR[, j+1]))
tmp.out <- smoother(tmp[, 1], tmp[, 2], method = "lowess", f = f[i])
lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20,
cex = 0.5, col = i)
}
}
text(10,160, "C)", cex = 3)
legend(25,200, paste("f : ", f), pch = 20, lwd = 2, col = 1:length(f))
plot(NA, NA, xlim = c(10,40), ylim = c(-50,200), xlab = "time [min]",
ylab = "Voltage [micro V]",
main = "ccPCR - Friedman's ''super smoother''")
span <- seq(0.01,0.05,0.01)
for (i in 1:length(span)) {
for (j in c(1,3,5,7)) {
tmp <- data.frame(na.omit(capillaryPCR[, j]),
na.omit(capillaryPCR[, j+1]))
tmp.out <- smoother(tmp[, 1], tmp[, 2], method = "supsmu",
span = span[i])
lines(data.frame(tmp[, 1], tmp.out), type = "l", pch = 20,
cex = 0.5, col = i)
}
}
text(10,160, "D)", cex = 3)
legend(25,200, paste("span : ", f), pch = 20, lwd = 2, col = 1:length(span))
par(mfrow = c(1,1), cex = 1)
}
\keyword{ datasets }
\keyword{ capillary }